1. Field of the Invention
The invention relates to methods of advertising analysis; more specifically, the invention relates to methods for determining the effectiveness of at least one medium of advertising, particularly in a multi-media or cross-media advertising campaign. The inventive method is used in part to determine one medium's effectiveness isolated from the effects of other media.
2. Description of the Related Art
Marketers face an increasingly challenging advertising environment. Media channels continue to fragment, and audiences are elusive. The imperative to reach consumers with a consistent message across multiple points of contact comes in the midst of advertising/marketing budget cuts and intense competition. These challenges occur while an emerging medium, the Internet, has attracted advertising dollars from major marketers. A series of industry studies have proven that the web has branding as well as direct response value.
The advent of integrated, cross-media campaigns that include the Internet has spurred marketers to explore how different media can be used synergistically to build their brands. And while branding objectives may be unified across media, the understanding that media have inherent differences, and costs, has led marketers to try to build competitive advantage by honing the efficiency of their cross-media investments. Many methodologies for measuring the impact of various marketing components fall short in measuring online advertising, so new methodologies, which take advantage of digital ad delivery and more cost-efficient data collection techniques, have come to the fore.
This new kind of research, which began with the present invention, relies primarily on experimental design to arrive at the findings related to relative media effectiveness and cost-efficiency, and Secondarily the research relies on the electronic real time ad delivery tracking and survey data for opportunity-to-see (OTS) to develop a more refined direction for implementing the re-allocation. Other approaches to cross-media measurement look similar in reporting and data collection to the inventive method but derive their findings from a different approach for combining electronic tracking and survey data for OTS measurement and/or the implementation of exposed/not exposed pseudo experimental designs. These other approaches of Cross Media Measurement (CMM) and Internet Cross-Media Measurement (ICM), bring with them some key challenges in ensuring proper analysis and interpretation of results.
Many of the techniques that marketers employ to measure their marketing mix do not work well for measuring the Internet and digital components of most cross-media campaigns. Over the past several decades, various methodologies have been developed and employed to measure the effectiveness of different marketing platforms within an integrated campaign. When measuring offline marketing channels only, these methodologies can be valuable in determining the overall relative efficiency of the efforts. However, many of these methodologies were developed before the advent of digital marketing and were not designed to quantify the Internet's (or other digital media's) contribution. In many cases, inherent aspects of those methodologies render them inadequate in measuring digital marketing activity.
While each of the following existing methodologies have been employed in limited cases to measure the value of digital marketing alone or as components of integrated strategies, they do not currently appear to be viable techniques for measuring the vast majority of online advertising or integrated marketing campaigns.
Some of those methodologies include:    Media Mix Modeling—Media mix modeling analyzes the efficiency of marketing activity by correlating data such as attitudes and sales with marketing spending and promotional activity. Since online marketing spending is generally a very small proportion of marketing budgets, these methodologies usually cannot accurately measure the Internet's contribution to the media mix unless Online Advertising spending is substantially increased. This typically would cost a marketer several million dollars in online advertising spending to reach the suggested threshold of reach. Modeling is also very expensive and cost-prohibitive to measure individual campaigns.    Tracking—Using telephone, mail or Internet based tracking for day-after-recall of advertising and general brand tracking is common, but it has drawbacks when online advertising is in the mix. The most significant drawback is the difficulty in using Random Digit Dial (RDD) methods to locate exposed respondents to online campaigns, which generally do not achieve high national reach. Another problem relates to the fact that online advertising is served in rotation; different visitors to the same web pages may be exposed to different advertisements. Thus, when it comes to the web, gauging vehicle exposure through surveys cannot effectively determine advertising exposure in most cases.    Split (or Matched) Market Testing—This methodology is perhaps the most promising as it does not require multi-million dollar national spending inherent in the media mix model approach, and is considered a gold-standard in measuring such media as radio. This approach works by measuring the effect of marketing activity in a test market and compares sales results to a matched market in which there is no marketing activity. In concept, this approach is highly defensible, but in practice this methodology cannot effectively be employed with online advertising because geo-targeting on the Internet is not yet sufficient to saturate a specific market with online advertising and block advertising from a matched region. Nor is this approach integrated with cross-media analysis. It is designed as a stand alone analysis of a single media.
Typical CMM & ICM uses “as it falls” sampling. ICM leverages the ability to track exposure to online advertising (with electronic “cookies”), but does not segment audience into exposed/control groups and cost-efficiently sample respondents to overcome some of the challenges described above. Two ads are randomly served with two separate cookies. After the survey is completed, respondents are analyzed in terms of the cookie, which is either “exposed” or “not exposed”. This may result in significant error for several reasons to be explained below.
Internet technology allows the research to precisely establish and determine control and exposed groups relative to online advertisements. Internet recruitment can also be extremely cost-effective, with little marginal cost (except respondent incentives) in recruiting large samples.
Utilizing digital media such as the Internet for recruitment creates a number of issues and challenges. When attempting to use an online surveying technique to gauge overall media-mix brand effectiveness, a fundamental concern is how accurately web surveys capture an audience that reflects a normal distribution of online and offline media behavior. Since the Internet is not yet ubiquitous, the potential exists for the results of the online surveys to be biased towards the specific proclivities of the Internet universe. The demographics of the Internet universe and the non-Internet universe reveal that the two populations are not equal. The Internet universe remains younger, more affluent, and higher educated. With that in mind, it is certainly conceivable that the online and offline groups of people will view media in different patterns and will respond to media in different ways. Thus, using an Internet-based sample to project cross-media effects to the entire US could encounter some error.
A potential concern is that Internet and non-Internet universes may differ in media consumption of television, print, radio and other offline media. In general, heavier Internet users tend to be lighter television viewers than non-Internet users. Further, a study by IMS and Doubleclick confirmed this notion by showing that targeting advertising toward heavier Internet users fills in the gaps that offline advertising leaves among the lightest offline viewers. In their study, delivering Internet advertising especially to heavier Internet users added disproportionate GRPs to the lightest offline quintiles.
FIG. 9 illustrates the specific difficulty in using the basic ICM online study universe to represent total US media usage patterns. The chart represents the television delivery of an actual packaged-goods advertiser's schedule. In this example, the television schedule delivers 455 GRPs to the total US Female 25-49 target. However, when the GRPs are decomposed against the Internet and non-Internet universes, it becomes clear that the two groups have received unequal media weight; the online population received 28% fewer GRPs than the offline population (420 GRPs vs. 537 GRPs). As a result of the disparate media weights, the online and offline segments of the Female 25-49 target could likely have received differential branding impact from the television schedule. In this example, interviewing survey respondents from the online universe in a basic ICM study may not have given a representative view of the overall TV impact.
Collecting online samples that mirror US media consumption is further complicated by another feature of online recruiting: the heaviest online users are the most likely to be invited to participate in the research. It has already been shown that the heaviest online users have the greatest skew towards light television viewing. Since these heavy Internet users typically comprise the greatest portion of the online sample in a basic ICM study, the potential to get a non-representative view of total offline media behavior becomes enlarged. Also, since heavy Internet users tend to be light television viewers, the net impact of the heavy user bias in recruitment is that online surveys will tend to over-sample the lightest television viewers and may result in an underestimation of the impact of TV ad campaigns. Of course, if the heaviest TV quintile is receiving 16 TV ads in 30 days, by undersampling these heavier TV users, ICM type studies could also make TV look more efficient than it really is by under-reporting those that have been over-delivered.
It has been demonstrated in public forums how conducting surveys within the average online ad schedule would tend to attract the heaviest online users if using the methods of CCM and ICM. Based on the page consumption patterns of a real site, the simulation distributed page views to users in five quintiles. Simulated ad schedules were then created that represented varying Shares of Voice (SOV), or portions of the total pages served. At typically low SOV levels for online recruitment of 3% to 10%, the simulation (FIG. 10) showed that the heaviest Internet users had a dramatically higher likelihood of being asked to participate compared to the lighter Internet users. Over 60% of the potential sample come from the top two quintiles, while fewer than 20% come from the bottom two quintiles. It is not until one achieves very high SOVs that the percentage of possible survey participants flattens out across the quintile groups.
This lopsidedness among the quintiles presents specific difficulties for extrapolating cross-media effects from an online sample and may also pose problems to the basic design of Online advertising effectiveness studies that use a simple “exposed/not exposed” research design instead of a true experimental design.
As online advertising became a larger component of advertising dollars there was a clear need for advertisers to integrate online impressions into their Marketing Mix Models. In order for a specific media to be isolated accurately in a multi linear regression marketing mix model the media weight variable for each media should be input into the model at the most granular level possible, however online is not currently bought and sold in the same way that other media is. Most other media is input to the model as Gross Rating Points (or Targeted Ratings Points) by Market (DMA) by week. The multi linear regression then looks for relationships between the media delivery and changes in sales volume to determine how much each media is driving sales. Online advertising is not currently bought or monitored in the same currency as other media. Advertisers and marketing mix modeling companies have attempted to use different variables for Online to include it into the marketing mix model but this can cause the relative comparison of online advertising to other media to be inaccurate.
Some of the inaccurate methods which have been used to integrate online into Marketing Mix Models are 1) using national impression inputs 2) clicks on ads 3) Consumer Panel Projections and 4) Ad server regional reporting.
Using national impression inputs will produce an inaccurate picture of online advertising because online is generally a smaller reach media which may not be measurable due to the error factor in the Marketing Mix Model. The Marketing Mix Model becomes more accurate with more variability and when the online media weight stream is limited to only national weekly information rather than weekly information broken down by market the chances for accurate measurement become slim.
Using clicks on an online ad as the input for the Marketing Mix Model misses major parts of the online advertising media weight and it not a similar currency to the other media being used in the model. This would be similar to the practice of only including telephone calls produced by a magazine ad as the input for magazine media weight into the marketing mix model which is generally not the data stream used for Marketing Mix Modeling.
Consumer Panel projections for the weekly GRP or TRP by market is only as reliable as the panel is down to the market by market level. Often times Consumer Panels are validated against the National Census population demographics which is valuable for some research. In the case of the weekly GRP reporting by market the reliability of the panel between markets is critical to the accuracy of the Marketing Mix Model output. If the panel is not representative at the individual market level these differences will result in different GRP levels reported where there really was differences. See FIG. 17 for comparisons of Consumer Panel data to the survey data collected directly from the campaign. The Consumer Panel also does not adjust the projections for the impressions that are delivered outside of the United States. The Consumer Panel projected to the national level may include impressions delivered outside of the US.
Because of the infrastructure of the Internet some companies have tried to leverage the third party ad servers as a source for the weekly GRP information. The third party ad serving tool hosts or holds the creative file for an advertiser then each publisher or website sources the file from the third party ad server. When the virtual request is made from the website to the third party ad server to deliver a particular ad the ad server can read the IP address of the server making the request for the ad. This IP address can then be associate with the zip where the Internet Service Provider is located. Again because of the infrastructure of the Internet, the consumer using the computer may be connecting through an Internet Service Provider that is in a different city. This can again cause problems because the media weight is being delivered into a different market than is being reported by the third party ad server.
All of the previously explained methodologies produce Online advertising inputs for the Marketing Mix Model which face one or both of these problems: 1) inaccurately grouping impressions into regions, and 2) providing inadequate or inappropriate data on which to model Online advertising.
A number of other problems exist in conventional ICM methods. ICM methods simply put contaminated respondents in the exposed group. This leads to measurement error. ICM methods also fail to measure the decay of Online advertising; worse, these methods can bias analysis toward Online by surveying consumers right after exposure.
Further, ICM methods typically fail to properly account for frequency of ad exposure. Frequency of ad exposure is a key determinant in brand metrics. Empirical research has shown that the absorption is convex linear, meaning that at a certain point, diminishing returns limit the incremental value of additional advertising impression. For ICM research, a vital prerequisite to the budget reallocation projection is the ability to accurately capture advertising frequency for individual media, in order to observe effects at varying levels of exposure. Some ICM research uses ad frequency estimates as the cornerstone for projecting Internet advertising effects at higher Internet spend (and therefore higher frequency) levels. Conversely, this reallocation of dollars to the web is counterbalanced by reduced offline spend, resulting in lower offline ad frequency. And, estimates of the relationship between offline frequencies and ad effectiveness are incorporated into the offline projections at the reduced spend levels. However, in ICM research, there are differences in the way frequency is estimated for each medium. In this type of research, Internet and print frequency estimates are more precise than estimates of TV frequency, and this may impact the ability to accurately tie frequency estimates with advertising effectiveness measures analyzing online frequency require matching those exposed to those delivered control ads by frequency. Otherwise, one is mistaking heavier usage of the media with advertising effect. The old approach used by CCM and ICM mismatches frequency because those in the not exposed group systematically have lighter frequency than the exposed group. This is a consequence of not using a true exposed/control design and a consequence of not measuring frequency properly.
Proper measurement analyzes advertising effect analyzes one variable (such as frequency) while holding other factors such as the size of the ads and the context of delivery constant. The old approach simply analyzes each variable (such as frequency) in a bi-variate fashion and does not simultaneously analyze or control for other collinear factors.
Issues arise in other specific media as well. In TV measurement, the ICM approach uses the Online campaign sample as representative TV sample. However, since heavier online users are lighter TV users, sampling an online campaign, which by nature will skew toward heavier users, will bias the sample and result in an understatement of TV's impact and shared interpretation of TV's diminishing returns. Also, in an attempt to account for non-online populations, the ICM approach may use telephone and online, but does not integrate the two data sets. Also, measuring TV without accounting for the reach curve in TV can dramatically underestimate TV's effect and bias the analysis in favor of online. The ICM approach makes no adjustment for TV's reach.
The ICM approach also attempts to guess which respondents have been exposed to TV advertising, but has no way of cross-checking this assignment. Analyzing TV requires measurement beyond simple “pre” and “post” summaries. The ICM approach only measures pre-post branding gains. But for some campaigns, TV's effect is to maintain high branding levels, therefore proper analysis of TV considers the maintenance effect of TV, not just the “pre-post” build.
Concerning competitive effects, ICM does not consider or calculate the effects of competitive advertising on the measured brand. As for attitudinal/branding and sales effects, ICM measured just attitudinal/branding but not both attitudinal/branding and sales. Further, the conventional Media Mix Modeling approach, which measures the impact of offline advertising and marketing on sales fails to accurately measure Online advertising because the Media Mix Models have used online impressions or click through to put online into the marketing mix model, even though all other media are measured in terms of gross rating points (GRPs) or targeted rating points (TRPs). There has been no method for converting Online advertising campaign impressions into GRPs by specific geographic region, which is the necessary format for Marketing Mix Models.
In terms of online media presence in media mix models, ICM measured television on a pre-post basis only, and, in one case, it plotted diminishing returns by quintile. When attempting to measure sales in a test market, Marketing Mix Models uses matching cities, or geographies for matching markets, or an exposed and not exposed as it falls design as mentioned above (in the case of AC Nielsen). Each approach is either not feasible for Online advertising or is less accurate than the invention.
Another main drawback to conventional ICM methods lay in their inability to break down the individual effects of sub-components of the ad buy within a medium; they were only able to report the aggregated effect of each medium and not report the contribution of individual ads and placements in an accurate manner.
The prior art also had significant shortfalls in connection with measuring the reach of print advertising as well. In determining magazine readers versus non-readers, the conventional approach examines magazine readers based on a survey question and then analyzes readers against everyone else. In determining exposed versus control readers and pre-post for same magazine/newspaper, and in taking controlled circulation measurements, the conventional approach simply compared pre-post magazine newspaper effects.
Generally, the prior art also used GRPs, reach, and frequency for optimization, had no way of measuring events, as significant error may result under such methodologies.
Accordingly, there is a long-felt need to create a marketing measuring method that overcomes the above deficiencies. More specifically, there is a long-felt need to assess the complementary effects of cross-media campaigns. There is a long-felt need to dissect the results of a branding campaign to understand the individual contribution of each medium. There is also a long-felt need to measure the cost efficiency of different media for key marketing objectives.